| Title |
Use of metabolomics for the identification and validation of clinical biomarkers for preterm birth: Preterm SAMBA
|
|---|---|
| Published in |
BMC Pregnancy and Childbirth, August 2016
|
| DOI | 10.1186/s12884-016-1006-9 |
| Pubmed ID | |
| Authors |
Jose G. Cecatti, Renato T. Souza, Karolina Sulek, Maria L. Costa, Louise C. Kenny, Lesley M. McCowan, Rodolfo C. Pacagnella, Silas G. Villas-Boas, Jussara Mayrink, Renato Passini, Kleber G. Franchini, Philip N. Baker, for the Preterm SAMBA and SCOPE study groups |
| Abstract |
Spontaneous preterm birth is a complex syndrome with multiple pathways interactions determining its occurrence, including genetic, immunological, physiologic, biochemical and environmental factors. Despite great worldwide efforts in preterm birth prevention, there are no recent effective therapeutic strategies able to decrease spontaneous preterm birth rates or their consequent neonatal morbidity/mortality. The Preterm SAMBA study will associate metabolomics technologies to identify clinical and metabolite predictors for preterm birth. These innovative and unbiased techniques might be a strategic key to advance spontaneous preterm birth prediction. Preterm SAMBA study consists of a discovery phase to identify biophysical and untargeted metabolomics from blood and hair samples associated with preterm birth, plus a validation phase to evaluate the performance of the predictive modelling. The first phase, a case-control study, will randomly select 100 women who had a spontaneous preterm birth (before 37 weeks) and 100 women who had term birth in the Cork Ireland and Auckland New Zealand cohorts within the SCOPE study, an international consortium aimed to identify potential metabolomic predictors using biophysical data and blood samples collected at 20 weeks of gestation. The validation phase will recruit 1150 Brazilian pregnant women from five participant centres and will collect blood and hair samples at 20 weeks of gestation to evaluate the performance of the algorithm model (sensitivity, specificity, predictive values and likelihood ratios) in predicting spontaneous preterm birth (before 34 weeks, with a secondary analysis of delivery before 37 weeks). The Preterm SAMBA study intends to step forward on preterm birth prediction using metabolomics techniques, and accurate protocols for sample collection among multi-ethnic populations. The use of metabolomics in medical science research is innovative and promises to provide solutions for disorders with multiple complex underlying determinants such as spontaneous preterm birth. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 29% |
| United Kingdom | 1 | 14% |
| Australia | 1 | 14% |
| India | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Unknown | 111 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 20 | 18% |
| Researcher | 17 | 15% |
| Student > Master | 12 | 11% |
| Student > Ph. D. Student | 11 | 10% |
| Student > Postgraduate | 7 | 6% |
| Other | 15 | 13% |
| Unknown | 31 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 23% |
| Nursing and Health Professions | 12 | 11% |
| Biochemistry, Genetics and Molecular Biology | 9 | 8% |
| Agricultural and Biological Sciences | 8 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 4% |
| Other | 17 | 15% |
| Unknown | 37 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 October 2016.
All research outputs
#14,078,546
of 24,885,505 outputs
Outputs from BMC Pregnancy and Childbirth
#2,482
of 4,641 outputs
Outputs of similar age
#195,996
of 373,070 outputs
Outputs of similar age from BMC Pregnancy and Childbirth
#60
of 102 outputs
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